In our experiments, the Adam [1]} optimizer is used with a learning rate of \(0.0005\) .
The batch size of the model is set to 512.
To prevent overfitting, we use dropout technique, and set it to 0.1.
The negative sampling size is set to 128.
And the dimension \(d\)  of each embedding vector \({\mathbf {e}}_0, {\mathbf {e}}_1, {\mathbf {r}}_0, {\mathbf {r}}_1, {\mathbf {r}}_2 \in \mathbb {R}^{d}\)  is set to 200.
The maximum number of training steps is 300,000.
We validate the model every 20 thousand steps.
The number of anchors for NodePiece are 20,000.
And \(\gamma \)  in the loss function is set to 6.
These hyper-parameters are selected according to the performance on the validation set.
